Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.
We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.
We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Multi-omic insights into microbiome function and composition typically advance one study at a time. However, to understand relationships across studies, they must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome comparison platform, which we demonstrate with Human Microbiome Project and iHMP data.
In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.
Single-walled carbon nanotubes (CNTs) emit heat when they absorb energy from near-infrared (NIR) light. Tissue is relatively transparent to NIR, which suggests that targeting CNTs to tumor cells, followed by noninvasive exposure to NIR light, will ablate tumors within the range of NIR. In this study, we demonstrate the specific binding of antibody-coupled CNTs to tumor cells in vitro, followed by their highly specific ablation with NIR light. Biotinylated polar lipids were used to prepare stable, biocompatible, noncytotoxic CNT dispersions that were then attached to one of two different neutralite avidin-derivatized mAbs directed against either human CD22 or CD25. CD22 ؉ CD25 ؊ Daudi cells bound only CNTs coupled to the anti-CD22 mAb; CD22 ؊ CD25 ؉ activated peripheral blood mononuclear cells bound only to the CNTs coupled to the anti-CD25 mAb. Most importantly, only the specifically targeted cells were killed after exposure to NIR light.immunoconjugates ͉ lymphoma cells ͉ monoclonal antibodies ͉ nanotechnology ͉ near infrared light
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